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Analogical Dissimilarity: definition, algorithms and first experiments in machine learning

Laurent Miclet 1 Arnaud Delhay 1
1 CORDIAL - Human-machine spoken dialogue
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, INRIA Rennes, ENSSAT - École Nationale Supérieure des Sciences Appliquées et de Technologie
Abstract : This paper defines the notion of analogical dissimilarity between four objects, with a special focus on dissimilarity between objects structured as sequences. Firstly, it studies the case where the four objects have a null analogical dissimilarity, i.e. are in an analogical relation. Secondly, when one of these objects is unknown, it gives algorithms to compute it. In particular, it studies a new formulation of solving analogical equations on sequences, based on the edit distance between strings. Thirdly, it tackles the problem of defining analogical dissimilarity, which is a measure of how close four objects are from being in analogical relation. To finish, it gives learning algorithms, i.e. methods to find the triple of objects in a learning sample which has the least analogical dissimilarity with a given object.
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https://hal.inria.fr/inria-00070321
Contributor : Rapport de Recherche Inria <>
Submitted on : Friday, May 19, 2006 - 8:04:12 PM
Last modification on : Thursday, February 11, 2021 - 2:48:04 PM
Long-term archiving on: : Tuesday, February 22, 2011 - 11:42:01 AM

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  • HAL Id : inria-00070321, version 1

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Laurent Miclet, Arnaud Delhay. Analogical Dissimilarity: definition, algorithms and first experiments in machine learning. [Research Report] RR-5694, INRIA. 2005, pp.60. ⟨inria-00070321⟩

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